Patchscopes: A Unifying Framework for Inspecting Hidden Representations of Language Models
(2401.06102)Abstract
Inspecting the information encoded in hidden representations of LLMs can explain models' behavior and verify their alignment with human values. Given the capabilities of LLMs in generating human-understandable text, we propose leveraging the model itself to explain its internal representations in natural language. We introduce a framework called Patchscopes and show how it can be used to answer a wide range of questions about an LLM's computation. We show that prior interpretability methods based on projecting representations into the vocabulary space and intervening on the LLM computation can be viewed as instances of this framework. Moreover, several of their shortcomings such as failure in inspecting early layers or lack of expressivity can be mitigated by Patchscopes. Beyond unifying prior inspection techniques, Patchscopes also opens up new possibilities such as using a more capable model to explain the representations of a smaller model, and unlocks new applications such as self-correction in multi-hop reasoning.
Overview
-
Patchscopes is a new framework that deciphers hidden representations in LLMs to explain their decision-making in natural language.
-
It integrates various interpretability methods, overcoming limitations of earlier techniques that struggled with early layers or lacked expressive language explanations.
-
The framework allows for robust multi-layer inspection without needing supervised training and uses a more advanced model to interpret a simpler one.
-
Patchscopes improves accuracy in next-token prediction and attribute extraction tasks, particularly in analyzing context understanding.
-
The framework has practical uses in error correction and shows promise for expanded applications in self-interpreting LLMs and alignment with human reasoning.
Understanding Hidden Representations in Language Models
Introduction
Examining the underlying representations in LLMs is invaluable for comprehending their decision-making processes and ensuring alignment with human values. While LLMs excel in generating coherent text, researchers have realized that these models can also serve as their own interpreters. A new framework has emerged, known as Patchscopes, which systematically decodes information embedded within LLM representations and produces explanations in natural language.
Unifying Interpretability Techniques
Patchscopes offers a modular approach which subsumes several previous interpretability methods under its umbrella. Classical methods typically either employed linear classifiers as probes on top of hidden layers or projected hidden layer representations into the vocab space to make sense of model predictions. However, these often failed in the early layers or lacked the expressiveness provided by a detailed language-based explanation.
The Patchscopes framework, on the other hand, patches targeted model representations into a specifically designed inference prompt, enabling the analysis of a model's reasoning at any layer. This not only unifies but also addresses the shortcomings of previous techniques by obviating the need for supervised training and allowing for robust inspection across multiple layers of the LLM.
Advancing Model Interpretability
Patchscopes introduce capabilities that prior tools have not explored. In particular, the framework can probe how LLMs process input tokens in the initial layers, revealing the contextualization and entity resolution strategies employed by the model. Interestingly, it also boosts expressiveness by utilizing a more advanced model to interpret the inner workings of a less sophisticated one.
Researchers have demonstrated Patchscopes' efficacy in a variety of experimental settings. For instance, it significantly improves the accuracy of next-token prediction when compared to existing projection-based methods. It also outperforms traditional probing in the extraction of specific attributes from LLM representations, particularly shining in tasks that require fine-grained analysis of the model’s understanding of context.
Practical Applications and Future Work
Beyond interpretability, Patchscopes have shown practical value in helping models self-correct multistep reasoning errors. By strategically rerouting representations during inference, the framework enhances the model's ability to connect separate reasoning steps and arrive at a coherent conclusion.
This framework only begins to explore the potential applications of self-interpreting LLMs. Future research could expand Patchscopes to other domains, develop variations for multitoken analysis, and construct guidelines for deploying task-specific Patchscopes. The push towards understanding the cognitive underpinnings of AI not only promotes transparency but also paves the way for models that better align with human reasoning.
Create an account to read this summary for free: